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Home Research Research Library Primary Care Practices’ Abilities And Challenges In Using Electronic Health Record Data For Quality Improvement Primary Care Practices’ Abilities And Challenges In Using Electronic Health Record Data For Quality Improvement 2018 Author(s) Cohen, Deborah J, Dorr, D A, Knierim, K, DuBard, C Annette, Hemler, J R, Hall, J D, Marino, Miguel, Solberg, Leif I, McConnell, K J, Nichols, L M, Nease, D E, Edwards, S T, Wu, W Y, Pham-Singer, H, Kho, A N, Phillips, Robert L, Rasmussen, L V, Duffy, F D, and Balasubramanian, Bijal A Topic(s) Role of Primary Care, and Achieving Health System Goals Keyword(s) Health Information Technology (HIT), Measurement, Payment, and Quality Of Care Volume Health Affairs Source Health Affairs Federal value-based payment programs require primary care practices to conduct quality improvement activities, informed by the electronic reports on clinical quality measures that their electronic health records (EHRs) generate. To determine whether EHRs produce reports adequate to the task, we examined survey responses from 1,492 practices across twelve states, supplemented with qualitative data. Meaningful-use participation, which requires the use of a federally certified EHR, was associated with the ability to generate reports-but the reports did not necessarily support quality improvement initiatives. Practices reported numerous challenges in generating adequate reports, such as difficulty manipulating and aligning measurement time frames with quality improvement needs, lack of functionality for generating reports on electronic clinical quality measures at different levels, discordance between clinical guidelines and measures available in reports, questionable data quality, and vendors that were unreceptive to changing EHR configuration beyond federal requirements. The current state of EHR measurement functionality may be insufficient to support federal initiatives that tie payment to clinical quality measures. ABFM Research Read all 2018 Practice Rurality of Family Physicians Enrolled in a Practice Transformation Network Go to Practice Rurality of Family Physicians Enrolled in a Practice Transformation Network 2024 Insights From a New National Academies Report on Caregiving Go to Insights From a New National Academies Report on Caregiving 2021 Life in Private Practice Go to Life in Private Practice 2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone
Author(s) Cohen, Deborah J, Dorr, D A, Knierim, K, DuBard, C Annette, Hemler, J R, Hall, J D, Marino, Miguel, Solberg, Leif I, McConnell, K J, Nichols, L M, Nease, D E, Edwards, S T, Wu, W Y, Pham-Singer, H, Kho, A N, Phillips, Robert L, Rasmussen, L V, Duffy, F D, and Balasubramanian, Bijal A Topic(s) Role of Primary Care, and Achieving Health System Goals Keyword(s) Health Information Technology (HIT), Measurement, Payment, and Quality Of Care Volume Health Affairs Source Health Affairs
ABFM Research Read all 2018 Practice Rurality of Family Physicians Enrolled in a Practice Transformation Network Go to Practice Rurality of Family Physicians Enrolled in a Practice Transformation Network 2024 Insights From a New National Academies Report on Caregiving Go to Insights From a New National Academies Report on Caregiving 2021 Life in Private Practice Go to Life in Private Practice 2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone
2018 Practice Rurality of Family Physicians Enrolled in a Practice Transformation Network Go to Practice Rurality of Family Physicians Enrolled in a Practice Transformation Network
2024 Insights From a New National Academies Report on Caregiving Go to Insights From a New National Academies Report on Caregiving
2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone